{"title":"Predicting Robotics Pedagogical Content Knowledge: The Role of Computational and Design Thinking Dispositions via Teaching Beliefs","authors":"Chung-Yuan Hsu, Meng-Jung Tsai","doi":"10.1177/07356331241236882","DOIUrl":null,"url":null,"abstract":"This research aimed to investigate the structural relationships among teachers’ computational thinking (CT), design thinking (DT), robotics teaching beliefs, and robotics pedagogical content knowledge (RPCK). A total of 98 in-service and pre-service teachers who participated in a robotics teaching professional development workshop served as the sample of the study. A survey including the Computational Thinking Scale, the Design Thinking Disposition Scale, the Robotics Teaching Beliefs Scale and the Technological Pedagogical Content Knowledge–Robotics Scale was conducted after the workshop. A confirmatory factor analysis was employed to validate the measurement constructs, and Partial Least Squares - Structural Equation Modeling (PLS-SEM) analysis was utilized to examine the relationships among the factors. The results revealed that both CT and DT dispositions could positively predict teachers’ robotics teaching beliefs, which subsequently predicted their RPCK. Moreover, a direct positive relationship between CT and RPCK was identified, while such a relationship was not evident for DT. The model demonstrates the critical role of CT in shaping teachers' beliefs and pedagogical strategies of robotics teaching, and provides insights into the indirect influence of DT. Finally, the Model of Robotics Teaching Professional Development (MRTPD) was proposed to profile how to promote teachers’ pedagogical content knowledge of robotics teaching from their CT and DT dispositions.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"12 1","pages":""},"PeriodicalIF":4.0000,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Educational Computing Research","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1177/07356331241236882","RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0
Abstract
This research aimed to investigate the structural relationships among teachers’ computational thinking (CT), design thinking (DT), robotics teaching beliefs, and robotics pedagogical content knowledge (RPCK). A total of 98 in-service and pre-service teachers who participated in a robotics teaching professional development workshop served as the sample of the study. A survey including the Computational Thinking Scale, the Design Thinking Disposition Scale, the Robotics Teaching Beliefs Scale and the Technological Pedagogical Content Knowledge–Robotics Scale was conducted after the workshop. A confirmatory factor analysis was employed to validate the measurement constructs, and Partial Least Squares - Structural Equation Modeling (PLS-SEM) analysis was utilized to examine the relationships among the factors. The results revealed that both CT and DT dispositions could positively predict teachers’ robotics teaching beliefs, which subsequently predicted their RPCK. Moreover, a direct positive relationship between CT and RPCK was identified, while such a relationship was not evident for DT. The model demonstrates the critical role of CT in shaping teachers' beliefs and pedagogical strategies of robotics teaching, and provides insights into the indirect influence of DT. Finally, the Model of Robotics Teaching Professional Development (MRTPD) was proposed to profile how to promote teachers’ pedagogical content knowledge of robotics teaching from their CT and DT dispositions.
期刊介绍:
The goal of this Journal is to provide an international scholarly publication forum for peer-reviewed interdisciplinary research into the applications, effects, and implications of computer-based education. The Journal features articles useful for practitioners and theorists alike. The terms "education" and "computing" are viewed broadly. “Education” refers to the use of computer-based technologies at all levels of the formal education system, business and industry, home-schooling, lifelong learning, and unintentional learning environments. “Computing” refers to all forms of computer applications and innovations - both hardware and software. For example, this could range from mobile and ubiquitous computing to immersive 3D simulations and games to computing-enhanced virtual learning environments.